Multidisciplinary design analysis and optimisation plays a key role in the conceptual design studies of novel aircraft configurations. Presently, there is a need to compare different classes of aircraft through equivalent methodologies to quantify the real differences and advantages, if any, that each configuration can offer. The GENUS aircraft design environment [27] aims to address this need through a common central architecture, as shown in Fig. 1. In GENUS, the former modules provide control variables for the later modules and constraints can be built to provide feedback connections. In this approach, GENUS becomes an effective synthesis, analysis and optimisation platform for aircraft conceptual design.
Geometry
The geometry module provides enough flexibility for the designers to model different configurations and controllability for design optimisations. The model is fully parametric, consisting of body components and/or lifting surfaces. A body component is a combination of linearly interpolated cross-sectional shapes defined by class shape transformation (CST) method [28], while lifting surfaces have NACA 4 series, NACA 5 series, NACA 6 series, wedge, double wedge, biconvex aerofoils, and the CST method as available input options. Different geometry formats are built in for the visualization and aerodynamic analysis.
Mission
The mission module defines the design specifications derived from the market or customer requirements, such as estimated maximum take-off mass, target range, cruise Mach number, payload, etc. This module provides the basic design variables for most of the modules. The F-design conditions’ performance can be analysed by changing variables in this module.
Mass breakdown
The mass breakdown module defines the estimated the mass, shape, dimensions of various components. At this stage, the structure and system mass build-up of novel configurations still rely on empirical methods derived from textbooks, such as Howe’s method [29], Raymer’s method [30], Cranfield lecture method, etc. Physical-based mass prediction methods are being developed to provide higher fidelity results.
Aerodynamics
There are multi-fidelity analysis methods available in the GENUS aerodynamic module. The main aerodynamic analysis tool for supersonic transport design is PANAIR [31]. PANAIR is a higher order subsonic and supersonic potential flow solver for arbitrary configurations. For aerodynamic analysis, PANAIR can provide lift coefficients and induced drag coefficients. The drag components for supersonic flight are denoted by Eq. (1):
$$C_{{\text{D}}} = C_{{{\text{D}}_{f} }} + C_{{{\text{D}}_{{{\text{wave}}}} }} + C_{{{\text{D}}_{i} }} ,$$
(1)
where \(C_{{\text{D}}}\) is the total drag coefficient, \(C_{{{\text{D}}_{f} }}\) is the friction drag coefficient,\({ }C_{{{\text{D}}_{{{\text{wave}}}} }}\). is the supersonic wave drag coefficient, \(C_{{{\text{D}}_{{\text{i}}} }}\) is the lift induced vortex drag coefficient.
The form factor method [32] is integrated to correct the zero-lift skin friction and form drag. The result comes from the contribution of each component, as shown in Eq. (2):
$$C_{{{\text{D}}_{f} }} = \mathop \sum \limits_{j = 1}^{N} \frac{{{\text{FF}}_{j} C_{{{\text{F}}_{j} }} S_{{{\text{wet}}_{j} }} }}{{S_{{{\text{ref}}}} }},$$
(2)
where N is the number of components used to model the configuration, \({\text{FF}}_{j}\) is the form factor of component \(j\), \(C_{{F_{j} }}\) is the skin friction coefficient of component \(j\), \(S_{{{\text{wet}}_{j} }}\). is the wet area of component \(j\), \(S_{{{\text{ref}}}}\) is the reference area of the total aircraft.
The supersonic area rule [33] is applied to calculate wave drag due to volume, as indicated in Eqs. (3) and (4). For accurate wave drag calculation, the Mach plane cross sectional area intersecting with the geometry is required:
$$C_{{{\text{D}}_{{{\text{wave}}}} }} \left( \theta \right) = - \frac{1}{2\pi }\mathop \smallint \limits_{0}^{l} \mathop \smallint \limits_{0}^{l} A^{\prime\prime}(x_{1} )A^{\prime\prime}\left( {x_{2} } \right)\ln \left| {x_{1} - x_{2} } \right|{\text{d}}x_{1} {\text{d}}x_{2}$$
(3)
$$C_{{{\text{D}}_{{{\text{wave}}}} }} = \frac{1}{2\pi }\mathop \smallint \limits_{0}^{2\pi } C_{{{\text{D}}_{{{\text{wave}}}} }} \left( \theta \right){\text{d}}\theta ,$$
(4)
where \(\theta\) is the angle between the Y-axis and a projection onto the Y–Z plane of a normal to the Mach plane, \({ }l\). is the overall aircraft length, \(A\left( x \right)\). is the Mach plane cross-sectional area at longitudinal coordinate \(x\), as illustrated in Fig. 2.
Propulsion
The propulsion module calculates the performance of a combination of available powerplants at different flight conditions (Mach, altitude, and throttle). The NASA EngineSim applet [34] has been adapted to simulate the thermodynamic analysis of turbojet, turbofan, and ramjet engines based on the component settings, as shown in Fig. 3.
Where A2 is the fan area, A8 is the nozzle area, BPR is the engine bypass ratio, Tmax is the maximum temperature, Tlimit is the temperature limit of the material, \(\rho \) is the density of the material.
Packaging and C.G.
Packaging is a novel and essential module in the GENUS environment. This module checks the accommodation of cabin, payload, fuel tanks, etc. Another function is to adjust the positions of inner components to achieve required static margin. In the case of supersonic transports, the wing volume is usually not enough for the mission fuel. Therefore, fuselage fuel tanks are introduced to the design. For the distributed fuel tanks, fuel consumption is scheduled to adjust the overall C.G. position and obtain better longitudinal stability. Landing gear loads, sizes and positions are also calculated in this module.
Performance
The performance module is designed to flexibly combine different segments, as shown in Fig. 4. Each segment consists of a set of control points. This module takes information from aerodynamic coefficients and propulsion performance maps. The next segment gets mass and C.G. updates from the previous segment. Each point is iterated by the kinetic and kinematic equations. In this way, the point and field performance can be predicted.
Stability and control
USAF Digital DATCOM [35] has been integrated into the GENUS environment through a legacy code linking process (Fig. 5). The stability characteristics and trim abilities at various flight conditions can be calculated and checked in this module.
Sonic boom prediction
Sonic boom constraints are crucial to the design of next-generation supersonic transport. In this research, there are two steps to predict the sonic boom intensity. The first step is to get the near-field pressure distribution. The second step is to propagate the near-field pressure to the ground through considering the nonlinear characteristics of the atmosphere.
The F-function theory developed by Whitham [15] and Walkden [16] is used for the near-field pressure calculation. The equivalent area due to volume and equivalent area due to lift are required for the near-field pressure calculation. The equation for the total effective area calculation is indicated in Eq. (5):
$$A_{{\text{e}}} \left( {x,\theta } \right) = A_{{\text{v}}} \left( {x,\theta } \right) + \frac{\beta }{{2q_{\infty } }}\mathop \smallint \limits_{0}^{x} L\left( {x,\theta } \right){\text{d}}x,$$
(5)
where \({A}_{\mathrm{e}}\left(x,\theta \right)\) is the equivalent area at coordinate \(x\) and angle \(\theta\), \({A}_{\mathrm{v}}\left(x,\theta \right)\) is the Mach plane cross sectional area at coordinate \(x\) and angle \(\theta\), \(\beta =\sqrt{{M}^{2}-1}\), \({q}_{\infty }\) is the ambient dynamic pressure, \(L(x,\theta )\) is the lift on a spanwise strip per unit chordwise length.
The F-function derives from the equivalent area, as shown in Eq. (6):
$$F\left( x \right) = \frac{1}{2\pi }\mathop \smallint \limits_{0}^{x} \frac{{A_{{\text{e}}}^{^{\prime\prime}} \left( {\overline{x},\theta } \right)}}{{\sqrt {x - \overline{x}} }}{\text{d}}\overline{x}.$$
(6)
The near-field pressure is then calculated based on the Whitham theory, as shown in Eq. (7):
$$\Delta p\left( x \right) = p_{0} \frac{{\gamma M^{2} F\left( \chi \right)}}{{\left( {2\beta r} \right)^{1/2} }},$$
(7)
where \(\Delta p=p-{p}_{0}\), \({p}_{0}\) is the ambient pressure, \(\gamma\) is the ratio of specific heat, \(M\) is the flight Mach number, \(r\) is the radius in polar coordinate.
The sonic boom propagates method is the waveform parameter method [36]. This method is based on geometrical acoustics and calculates the sonic boom signature directly with distance steps along a ray, which is more suitable for automatic computation. It is reprogrammed in JAVA and implemented as a special module method. A weakness of the method is that the code expects one shock formation or coalescence at a time. For a complex signature with large numbers of points specified, there is a big chance of failure. This becomes a problem when using CFD data as F-function inputs. Another problem with the propagation code is that it cannot predict the rise time of the ground signature. As a result, this method is not suitable for the perceived loudness level calculation. The validation of the sonic boom prediction methods can be found in Fig. 6.